Search

Funding Information: This study was supported by grants from the Academy of Finland (grants 278741, 313267, 326238, 333583, 313267, 326238, and 344698), Academy of Finland Centre of Excellence for Translational Cancer Biology (grant 271845), Cancer Society of Finland (grants 140114, 160080, 170112), Sigrid Jusélius Foundation. OK was also supported by Vetenskaprådet, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Vinnova. Open Access funding provided by University of Helsinki including Helsinki University Central Hospital. Funding Information: OK is a co-founder and a board member of Medisapiens and Sartar Therapeutics and has received royalty on patents licensed by Vysis-Abbot. His research group has a Vinnova-funded collaborative programme with Astra-Zeneca, Labcyte, Takara Biosciences and Pelago. VP has received a grant from UPM-Kymmene. Other authors did not disclose any potential conflicts of interest. Publisher Copyright: © 2022, The Author(s). Many efforts are underway to develop novel therapies against the aggressive high-grade serous ovarian cancers (HGSOCs), while our understanding of treatment options for low-grade (LGSOC) or mucinous (MUCOC) of ovarian malignancies is not developing as well. We describe here a functional precision oncology (fPO) strategy in epithelial ovarian cancers (EOC), which involves high-throughput drug testing of patient-derived ovarian cancer cells (PDCs) with a library of 526 oncology drugs, combined with genomic and transcriptomic profiling. HGSOC, LGSOC and MUCOC PDCs had statistically different overall drug response profiles, with LGSOCs responding better to targeted inhibitors than HGSOCs. We identified several subtype-specific drug responses, such as LGSOC PDCs showing high sensitivity to MDM2, ERBB2/EGFR inhibitors, MUCOC PDCs to MEK inhibitors, whereas HGSOCs showed strongest effects with CHK1 inhibitors and SMAC mimetics. We also explored several drug combinations and found that the dual inhibition of MEK and SHP2 was synergistic in MAPK-driven EOCs. We describe a clinical case study, where real-time fPO analysis of samples from a patient with metastatic, chemorefractory LGSOC with a CLU-NRG1 fusion guided clinical therapy selection. fPO-tailored therapy with afatinib, followed by trastuzumab and pertuzumab, successfully reduced tumour burden and blocked disease progression over a five-year period. In summary, fPO is a powerful approach for the identification of systematic drug response differences across EOC subtypes, as well as to highlight patient-specific drug regimens that could help to optimise therapies to individual patients in the future.

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool enabling class-free phenotypic supervised machine learning, to describe and explore biological data in a continuous manner. First, we compare traditional classification with regression in a simulated experimental setup. Second, we use our framework to identify genes involved in regulating triglyceride levels in human cells. Subsequently, we analyse a time-lapse dataset on mitosis to demonstrate that the proposed methodology is capable of modelling complex processes at infinite resolution. Finally, we show that hemocyte differentiation in Drosophila melanogaster has continuous characteristics., High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and present the Regression Plane module of Advanced Cell Classifier.

Here, we describe a scalable and automated, high-content microscopy -based mini-immunofluorescence assay (mini-IFA) for serological testing i.e., detection of antibodies. Unlike conventional IFA, which often relies on the use of cells infected with the target pathogen, our assay employs transfected cells expressing individual viral antigens. The assay builds on a custom neural network-based image analysis pipeline for the automated and multiplexed detection of immunoglobulins (IgG, IgA, and IgM) in patient samples. As a proof-of-concept, we employed high-throughput equipment to set up the assay for measuring antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with spike (S), membrane (M), and nucleo (N) proteins, and the receptor-binding domain (R) as the antigens. We compared the automated mini-IFA results from hundreds of patient samples to the visual observations of human experts and to the results obtained with conventional ELISA. The comparisons demonstrated a high correlation to both, suggesting high sensitivity and specificity of the mini-IFA. By testing pre-pandemic samples and those collected from patients with RT-PCR confirmed SARS-CoV-2 infection, we found mini-IFA to be most suitable for IgG and IgA detection. The results demonstrated N and S proteins as the ideal antigens, and the use of these antigens can serve to distinguish between vaccinated and infected individuals. The assay principle described enables detection of antibodies against practically any pathogen, and none of the assay steps require high biosafety level environment. The simultaneous detection of multiple Ig classes allows for distinguishing between recent and past infection.Public abstractThe manuscript describes a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The automated method builds on machine-learning -guided image analysis with SARS-CoV-2 as the model pathogen. The method enables simultaneous measurement of IgM, IgA, and IgG responses against different virus antigens in a high throughput manner. The assay relies on antigens expressed through transfection and allows for differentiation between vaccine-induced and infection-induced antibody responses. The transfection-based antigen expression enables performing the assay at a low biosafety level laboratory and allows fast adaptation of the assay to emerging pathogens. Our results provide proof-of-concept for the approach, demonstrating fast and accurate measurement of antibody responses in a clinical and research set-up.

Treatment options for COVID‐19, caused by SARS‐CoV‐2, remain limited. Understanding viral pathogenesis at the molecular level is critical to develop effective therapy. Some recent studies have explored SARS‐CoV‐2–host interactomes and provided great resources for understanding viral replication. However, host proteins that functionally associate with SARS‐CoV‐2 are localized in the corresponding subnetwork within the comprehensive human interactome. Therefore, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machineries used for viral processing. This study applied both affinity purification mass spectrometry (AP‐MS) and the complementary proximity‐based labeling MS method (BioID‐MS) on 29 viral ORFs and 18 host proteins with potential roles in viral replication to map the interactions relevant to viral processing. The analysis yields a list of 693 hub proteins sharing interactions with both viral baits and host baits and revealed their biological significance for SARS‐CoV‐2. Those hub proteins then served as a rational resource for drug repurposing via a virtual screening approach. The overall process resulted in the suggested repurposing of 59 compounds for 15 protein targets. Furthermore, antiviral effects of some candidate drugs were observed in vitro validation using image‐based drug screen with infectious SARS‐CoV‐2. In addition, our results suggest that the antiviral activity of methotrexate could be associated with its inhibitory effect on specific protein–protein interactions., A large‐scale proteomics study identifies critical host proteins for SARS‐CoV‐2 processing. Proteins from these core subnetworks are used for drug repurposing analyses, indicating drugs with antiviral effects.

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application’s ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at ( https://itrex.kitz-heidelberg.de ).

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20% of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, functional assays with patient-derived ex vivo 3D cell culture have gained importance for drug discovery and precision medicine. We recently evaluated the major advancements and needs for the 3D cell culture screening, and concluded that strictly standardized and robust sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and can select uniform sized or shaped spheroids to transfer them between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.

Identification of HLA class I ligands from the tumour surface (ligandome or immunopeptidome) is essential for designing T-cell mediated cancer therapeutic approaches. However, the sensitivity of the process for isolating MHC-I restricted tumor-specific peptides has been the major limiting factor for reliable tumor antigen characterization, making clear the need for technical improvement. Here, we describe our work from the fabrication and development of a novel microfluidic-based chip (PeptiCHIP) and to its use to identify and characterize tumor-speficic ligands on clinically relevant human organoids. Specifically, we assessed the potential of immobilizing a pan-HLA antibody on solid surfaces via well-characterized streptavidin-biotin chemistry, overcoming the limitations of the cross-linking chemistry used to prepare the affinity matrix with the desired antibodies in the immunopeptidomics workflow. Furthermore, to address the restrictions related to the handling and the limited availability of tumour samples, we further developed the concept towards the implementation of a microfluidic through-flow system. Thus, the biotinylated pan-HLA antibody was immobilized on streptavidin-functionalized surfaces, and immune-affinity purification (IP) was carried out on customized microfluidic pillar arrays made of thiol-ene polymer. Compared to the standard methods reported in the field, our methodology drastically reduces the handling, the amount of antibody and the time required for peptide isolation. In this work, we carefully examined the specificity and robustness of our customized technology for immunopeptidomics workflows. We tested this novel platform by immunopurifying HLA-I complexes from as few as 106 cells both in a widely studied B-cell line and in patients-derived ex vivo cell cultures. After the final elution in mild acid, HLA-I-presented peptides were identified by tandem mass spectrometry and further investigated by in vitro methods. These results highlight the potential to exploit microfluidics-based strategies in immunopeptidomics platforms and in personalized immunopeptidome analysis from cells isolated from individual tumour biopsies to design tailored cancer therapeutic vaccines.

Publisher Copyright: © 2021 The Authors. Published by American Chemical Society. Identification of HLA class I ligands from the tumor surface (ligandome or immunopeptidome) is essential for designing T-cell mediated cancer therapeutic approaches. However, the sensitivity of the process for isolating MHC-I restricted tumor-specific peptides has been the major limiting factor for reliable tumor antigen characterization, making clear the need for technical improvement. Here, we describe our work from the fabrication and development of a microfluidic-based chip (PeptiCHIP) and its use to identify and characterize tumor-specific ligands on clinically relevant human samples. Specifically, we assessed the potential of immobilizing a pan-HLA antibody on solid surfaces via well-characterized streptavidin-biotin chemistry, overcoming the limitations of the cross-linking chemistry used to prepare the affinity matrix with the desired antibodies in the immunopeptidomics workflow. Furthermore, to address the restrictions related to the handling and the limited availability of tumor samples, we further developed the concept toward the implementation of a microfluidic through-flow system. Thus, the biotinylated pan-HLA antibody was immobilized on streptavidin-functionalized surfaces, and immune-affinity purification (IP) was carried out on customized microfluidic pillar arrays made of thiol-ene polymer. Compared to the standard methods reported in the field, our methodology reduces the amount of antibody and the time required for peptide isolation. In this work, we carefully examined the specificity and robustness of our customized technology for immunopeptidomics workflows. We tested this platform by immunopurifying HLA-I complexes from 1 × 106 cells both in a widely studied B-cell line and in patients-derived ex vivo cell cultures, instead of 5 × 108 cells as required in the current technology. After the final elution in mild acid, HLA-I-presented peptides were identified by tandem mass spectrometry and further investigated by in vitro methods. These results highlight the potential to exploit microfluidics-based strategies in immunopeptidomics platforms and in personalized immunopeptidome analysis from cells isolated from individual tumor biopsies to design tailored cancer therapeutic vaccines. Moreover, the possibility to integrate multiple identical units on a single chip further improves the throughput and multiplexing of these assays with a view to clinical needs.

Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner. We have implementedRegression Plane (RP), the first user-friendly discovery tool enabling class-free phenotypic supervised machine learning.

Knowledge about naturally presented HLA class I ligands from the tumor surface (the ligandome or immunopeptidome) is essential for designing T-cell mediated cancer therapeutic approaches. Indeed, the generation of specific anti-tumor CD8+ T cells relies on the recognition of tumor-antigens in the HLA-I complex. However, the sensitivity of the process for isolating the tumor antigens have been the major limiting factor for reliable HLA-I peptide characterization, making clear the need for technical improvement.Here, we assessed the potential of immobilizing a pan-HLA antibody on solid surfaces via well-characterized streptavidin-biotin chemistry, overcoming the limitations of the cross-linking chemistry used to prepare the affinity matrix with the desired antibodies in the immunopeptidomic workflow. Furthermore, to address the restrictions related to the handling and the limited availability of tumor samples, we further developed the concept towards the implementation of a microfluidic through-flow system.Thus, the biotinylated pan-HLA antibody was immobilized on streptavidin-functionalized surfaces, and immune-affinity purification (IP) was carried out on customized microfluidic pillar arrays made of thiol-ene polymer. Compared to the standard methods reported in the field, our methodology drastically reduces the handling, the amount of antibody and the time required for peptide isolation.In this work, we carefully examined the specificity and robustness of our customized technology for immunopeptidomics workflows. We tested this novel platform by immunopurifying HLA-I complexes from as few as 106 cells both in a widely studied B-cell line and in patients-derived ex vivo cell cultures. After the final elution in mild acid, HLA-I-presented peptides were identified by tandem mass spectrometry and further investigated by in vitro methods. These results highlight the potential to exploit microfluidics-based strategies in immunopeptidomic platforms and in personalized immunopeptidome analysis from cells isolated from individual tumor biopsies to design tailored cancer therapeutic vaccines. Citation Format: Sara Feola, Markus Happala, Karita Peltonen, Cristian Capasso, Gabriella Antignani, Antonio Federico, Vilja Pietiäinen, Jacopo Chiaro, Michaela Feodoroff, Antti Ranniko, Manlio Fusciello, Satu Koskela, Jukka Partanen, Firas Hamdan, Sari Tähkä, Erkko Ylösmäki, Dario Greco, Mikaela Grönholm, Rui M M Branca, Janne Lehtiö, Tiina Siikanen, Vincenzo Cerullo. PeptiCHIP: A novel microfluidic-based chip platform for tumor antigen landscape identification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1897.

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. QC 20170808

The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell- and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates.

Ovarian cancer (OC) is difficult to treat and has a high mortality rate. There is a strong need for new therapies, but OC is often not the main indication for drug development activities. Hence, here we explored systematic drug repurposing based on clues from ex-vivo testing of human OC patient cells. We established patient-derived cancer cell (PDCs) cultures using ascites or tumor tissue from serous OC patients and applied them to ex vivo Drug Sensitivity and Resistance Testing (DSRT) with a panel of 528 approved and investigational oncology drugs. The representativity of the PDCs was confirmed by genomic and phenotypic profiling in comparison to the original tumor sample. Both 2D and 3D culture conditions were applied and compared for their drug responses. 9 PDCs and 11 high-grade serous OC cell lines were examined. High grade serous OC (HGSOC, 6 cases) with TP53 mutation and CCNE1, CCNE1/KRAS or MYC/KRAS amplifications showed highly resistant drug response profiles, which reflects the clinical challenges in treating this OC subtype. Some HGSOC PDCs showed vulnerability to SMAC mimetics, inhibitors of Apoptosis Protein Antagonists, (e.g. birinapant) and to prexasertib, a checkpoint kinase 1 and 2 inhibitor. In contrast, low-grade serous OC (LGSOC, 3 cases) showed subtype-specific sensitivity to several kinase inhibitors, including EGFR/Her2, MEK, mTOR and PI3K inhibitors. In a metastatic LGSOC patient, RNA-sequencing revealed a CLU-NRG1 fusion gene, which creates an autocrine activation loop of the ErbB family of receptor kinases. DSRT assay confirmed a strong sensitivity of the PDCs ex-vivo to dual-kinase inhibitors, including afatinib. Based on these data, the patient has received approved kinase inhibitors, including afatinib monotherapy followed by a combination of herceptin and pertuzumab. This strategy has managed to keep the disease in control for over 3 years as measured by clinical criteria, including imaging and serum CA125 antigen levels. In conclusion, systems precision cancer medicine with PDCs could provide a valuable approach to reveal patient-specific drug efficacies that can be translated to the clinic in real-time. Citation Format: Astrid Murumägi, Daniela Ungureanu, Suleiman Khan, Akira Hirasawa, Mariliina Arjama, Katja Välimäki, Piia Mikkonen, Wilhelmiina Niininen, Ashwini Kumar, Samuli Eldfors, Teijo Pellinen, Vilja Pietiäinen, Andrus Mägi, Riitta Koivisto-Korander, Johanna Tapper, Mikko Loukovaara, Tero Aittokallio, Ralf Bützow, Olli Kallioniemi. Clinical implementation of precision systems oncology in the treatment of ovarian cancer based on ex-vivo drug testing and molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2945.

The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.

We have set up a precision medicine strategy for solid tumors to i) understand biological heterogeneity and driver signaling pathways in cancer, ii) identify new drug opportunities, iii) develop biomarkers for drug responses, and iv) eventually tailor effective treatments for individual patients. Fresh cancer tissue is obtained directly from clinics and processed to provide patient -derived cells (PDCs). PDCs and original tumor tissues are characterized using genetic profiling and image-based phenotyping, phenomics. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the representative PDC models. The PDCs are plated in 384-well plates and treated with oncological compounds, each in five concentrations in 384-well plates. The cell viability and toxicity are measured as drug responses using plate readers. Alternatively, drug-treated cells are immunostained and subjected to automated high-content imaging and image analysis. Central to the success of this approach is to grow and test the patient derived cells in ex vivo conditions mimicking the tumor microenvironment. To this aim, we have developed 3D drug profiling for PDC spheroids from ovarian and renal cancers. For the 3D drug profiling, cells were cultivated either in Matrigel or in cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. The pipetting robot Biomek FXp (Beckman Coulter) was utilized for transfer of matrices and cells to 384-well plates, and acoustic dispenser Echo 550 (Labcyte) for delivery of tailored drug library of 52 drugs to the spheroids. The drug sensitivities of cancer cell spheroids were scored using cell viability measurement as well as high-content confocal imaging. Our results with primary cells and cell lines suggest that both Matrigel and cellulose-based hydrogel are applicable in the 384-well plate drug profiling assay, and PDC spheroids are formed in both conditions. When the drug responses of ovarian cancer PDCs grown in different 2D and 3D conditions were systematically compared, we observed significant differences in sensitivity to several drugs. Here, we describe the individual drug effects in all conditions, such as some chemotherapeutics being less effective in 3D. As a conclusion, the comparison of results from 2D and 3D drug profiling increases our understanding of mechanisms of drugs, and may aid to select the most representative drugs for the patient. Citation Format: Piia Mikkonen, Laura Turunen, Lauri Paasonen, Swapnil Potdar, Lassi Paavolainen, Astrid Murumägi, Olli Kallioniemi, Vilja M. Pietiäinen. Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5029.

Patient-derived tumor cells are highly variable in their morphology and molecular phenotypes and after exposure to a library of drugs their heterogeneity further increases as the cells respond to the perturbations. The complete phenotypic diversity of the tumor cells and drug response patterns may remain undetectable if only few simple readouts, such as individual biomarkers in pre-defined tumor cells, are considered. Therefore, novel techniques that capture and visualize the entire spectrum of heterogeneous phenotypic changes in ex-vivo patient-derived tumor cells are needed. Here, we applied unsupervised machine learning for comprehensive visualization of cell phenotypes with the aim to improve the analysis of image-based drug screening results. We performed high content screening of patient-derived renal clear cell carcinoma cells, exposed to 36 drugs in nine concentrations applied in 384-well format. The nuclei were stained with Hoechst and cell proliferation using Ki67, and images were acquired with a high-content imaging microscope (PE Operetta, 20x). To quantitatively describe the cell phenotypes, we used CellProfiler for cell segmentation and feature extraction and then applied a large-scale data embedding method (LargeVis) to cluster the cells based on their phenotypes. Using the clustering, we created an image-based phenotype-map to describe the phenotypic landscape. Overlaying the drug treatments on different concentrations on top of the map allowed for visualization of unique phenotypic fingerprints for each drug at the single cell resolution. Visual inspection of the phenotype-map displayed visually similar cells clustering together. Likewise, a comparison with an expert trained supervised classification indicated consistency with the phenotype-map. As expected, Ki67-positive and -negative cells as well as dying cells formed clusters. Interestingly, the phenotype-map identified sub-clusters within the Ki67-positive and -negative cells. Furthermore, the map also allowed us to adjust for classification errors occurring in the supervised classification. In conclusion, we find that phenotypic signatures provide an intuitive way of linking and comparing phenotypes of distinct populations of drug-treated cells at a single-cell level. This will reveal additional systematic information of the drug responses, complementing traditional readout, such as cell viability, by highlighting the phenotypic changes over concentrations. Citation Format: Riku Turkki, Lassi Paavolainen, Piia Mikkonen, Päivi Östling, Peter Horvath, Vilja Pietiäinen, Olli Kallioniemi. Phenotypic heterogeneity of patient-derived tumor cells visualized by unsupervised analysis in cell-based personalized drug testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5302.

Uterine leiomyomas (ULs) are extremely common smooth muscle tumors, occurring in an estimated 77% of women of reproductive age. Although benign, ULs form a major burden to women's health and are the leading cause of hysterectomy worldwide. Recent findings show that ULs can be classified into at least three distinct molecular subclasses, each with a characteristic genetic driver aberration and global gene expression profile: MED12 (mediator complex subunit 12) mutation-positive, HMGA2 (high mobility group AT-hook 2)-overexpressing, FH (fumarate hydratase)-deficient. However, ULs are currently treated without taking into account the possible different subclasses. The aim of this project is to develop and characterize 3D in vitro models of ULs. The models will be used to examine responses to existing treatments in different subclasses of UL and to identify subclass-specific novel lead compounds for drugs. The work is based on the hypothesis that different molecular UL subclasses have distinct sensitivity and resistance patterns to existing treatment options. Therefore, ULs should not be treated as a single entity. The study material consists of UL tissue belonging to different subclasses and myometrium, collected from hysterectomy patients during surgery in Helsinki University Hospital, Finland. Freshly operated samples are processed to initiate primary cell cultures. The primary cells are seeded on Ultra-Low Attachment (ULA) multiwell plates and grown to form 3D spheroids. In case of MED12-mutated tumors, the spheroids are screened for MED12 mutations by Sanger sequencing. MED12-wild type samples and spheroids are analyzed for overexpression of HMGA2 and FH-deficiency. Immunofluorescence staining is performed to characterize different cell types in the spheroid structure. The presence of live/dead cells in spheroids is monitored during the drug treatments in 384-well plates with high-content confocal microscopy. The approach will first focus on the most common subclasses (MED12-mutated and HMGA2-overexpressing tumors) and most common treatment options. Here, we have set up novel patient-derived cell models for ULs. We have characterized myometrium and UL-derived spheroids and show that they retain the characteristics and genetic driver aberration of the original samples. Furthermore, we show they are composed mostly of live cells. Subsequent in vitro high-throughput drug screening will lead to characterization of drug sensitivities and identification of novel lead compounds for drugs. This information is important for rational drug design, where the effects of candidate drugs can be evaluated in light of the molecular class of the respective tumors. This work will lead to new knowledge towards precision medicine, which has potential for scientific breakthrough, since it would avoid exposing the patients to ineffective treatment options. Citation Format: Simona Bramante, Vilja Pietiäinen, Lassi Paavolainen, Annukka Pasanen, Netta Mäkinen, Hanna-Riikka Heinonen, Pirjo Ikonen, Oskari Heikinheimo, Jari Sjöberg, Ralf Bützow, Lauri Aaltonen. Development of uterine leiomyoma 3D in vitro models for high-throughput drug and chemical compound screenings: Towards personalized medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1159.

Objective—ATP-binding cassette transporter A1 (ABCA1) is thought to lipidate apolipoprotein A-I (apoA-I) at the plasma membrane, with endosomal cholesterol contributing as substrate. The mechanisms of ABCA1 surface delivery are not well understood. We have shown that Rab8 regulates endosomal cholesterol removal to apoA-I in human fibroblasts. Here, we investigated whether Rab8 plays a role in ABCA1 plasma membrane expression and cholesterol removal in primary human macrophages.Methods and Results—We found that Rab8 was abundantly expressed in human atherosclerotic lesional macrophages and upregulated on lipid loading of macrophages in vitro. Adenoviral overexpression of Rab8 increased ABCA1 protein levels and reduced cholesterol deposition in macrophage foam cells incubated with apoA-I. Depletion of Rab8 decreased the fraction of ABCA1 at the plasma membrane and inhibited the efflux of lipoprotein-derived endosomal cholesterol to apoA-I. In Rab8-depleted cells, ABCA1-GFP localized in β1 integrin and transferrin receptor containing recycling organelles.Conclusion—Rab8 reduces foam cell formation by facilitating ABCA1 surface expression and stimulating endosomal cholesterol efflux to apoA-I in primary human macrophages.

Caveolin-1 binds cholesterol and caveola formation involves caveolin-1 oligomerization and cholesterol association. The role of cholesterol in caveolae has so far been addressed by methods that compromise membrane integrity and abolish caveolar invaginations. To study the importance of sterol specificity for the structure and function of caveolae, we replaced cholesterol in mammalian cells with its immediate precursor desmosterol by inhibiting 24-dehydrocholesterol reductase. Desmosterol could substitute for cholesterol in maintaining cell growth, membrane integrity, and preserving caveolar invaginations. However, in desmosterol cells the affinity of caveolin-1 for sterol and the stability of caveolin oligomers were decreased. Moreover, caveolar invaginations became more heterogeneous in dimensions and in the number of caveolin-1 molecules per caveola. Despite the altered caveolar structure, caveolar ligand uptake was only moderately inhibited. We found that in desmosterol cells, Src kinase phosphorylated Cav1 at Tyr14 more avidly than in cholesterol cells. Taken the role of Cav1 Tyr14 phosphorylation in caveolar endocytosis, this may help to preserve caveolar uptake in desmosterol cells. We conclude that a sterol C24 double bond interferes with caveolin-sterol interaction and perturbs caveolar morphology but facilitates Cav1 Src phosphorylation and allows caveolar endocytosis. More generally, substitution of cholesterol by a structurally closely related sterol provides a method to selectively modify membrane protein-sterol affinity, structure and function of cholesterol-dependent domains without compromising membrane integrity.

Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples,we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci,SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing. Refereed/Peer-reviewed

Echovirus 1 (EV1) is a human pathogen which belongs to the Picornaviridae family of RNA viruses. We have analyzed the early events of infection after EV1 binding to its receptor α2β1 integrin and elucidated the route by which EV1 gains access to the host cell. EV1 binding onto the cell surface and subsequent entry resulted in conformational changes of the viral capsid as demonstrated by sucrose gradient sedimentation analysis. After 15 min to 2 h postinfection (p.i.) EV1 capsid proteins were seen in vesicular structures that were negative for markers of the clathrin-dependent endocytic pathway. In contrast, immunofluorescence confocal microscopy showed that EV1, α2β1 integrin, and caveolin-1 were internalized together in vesicular structures to the perinuclear area. Electron microscopy showed the presence of EV1 particles inside caveolae. Furthermore, infective EV1 could be isolated with anti-caveolin-1 beads 15 min p.i., confirming a close association with caveolin-1. Finally, the expression of dominant negative caveolin in cells markedly inhibited EV1 infection, indicating the importance of caveolae for the viral replication cycle of EV1.

We have built up the precision medicine approach for solid tumors to understand biological heterogeneity and driver signalling pathways in cancer, to develop pharmacogenomic biomarkers and to ultimately tailor effective treatments for patients. In addition to genetic profiling of original tumor tissues, we have developed patient -derived tumor cell (PDC) models, and characterized these models with genomics, image-based phenotyping (phenomics) and high throughput drug profiling. Here, we describe our approach for a clear cell renal cell carcinoma (ccRCC) patient. PDC cultures were initiated using conditional reprogramming method from fresh primary tumor, protrusion to vena cava, and the benign tissue samples. The exome-sequencing of tissues and PDCs showed that several copy number variations and somatic driver mutations were shared between the cancer tissue and corresponding cell models, including TSC2 and VHL mutations. Using image-based phenotyping, the PDCs from the benign tissue were observed to be strongly positive to cytokeratin 7 (CK7), while original tumor tissue and PDCs from tumor tissue were predominantly CK7 negative. The PDCs expressed vimentin, a mesenchymal marker, but the epithelial marker E-cadherin was downregulated, suggesting for epithelial-mesenchymal transition. The PDCs from primary tumor were exposed to drug sensitivity profiling with a library of 525 approved and investigational oncology compounds in five different concentrations and imaged for proliferation with Ki-67. PI3K/mTOR pathway inhibitors were found to be among the drugs inhibiting the cell proliferation, in agreement with detected somatic mutations affecting these pathways. In addition, our image-based drug sensitivity testing revealed the intra-sample heterogeneity in drug responses and resistance, and we are now further investigating the mechanisms of most potent drugs and their combinations in PDCs at single cell level. As a conclusion, our results implicate the importance of comprehensive characterization of PDCs and their drug responses in translational research. We also foresee that these approaches may potentially improve the translation of results back to clinic and support the design of combination therapies in cancer. Citation Format: Vilja M. Pietiäinen, Piia Mikkonen, Khalid Saeed, Lassi Paavolainen, Teijo Pellinen, Swapnil Potdar, John Mpindi, Harry Nisén, Antti Rannikko, Tuomas Mirtti, Peter Horvath, Päivi Östling, Olli Kallioniemi. Precision medicine approach: analysis of renal cancer patient-derived cells with phenomics, genomics and drug sensitivity profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3854. doi:10.1158/1538-7445.AM2017-3854

To obtain a view of the influence of enterovirus infection on host cell gene expression, multiple cellular mRNA levels were first investigated during echovirus 1 (EV1) infection in HOS palpha2AW cells using cDNA array analysis. Visible cytopathic effect and partial shut-off of host cell protein synthesis were observed 6-10 h after the EV1 infection. Simultaneously, approximately 2% of the investigated genes, among them immediate-early response genes and genes involved in apoptotic pathways and cell growth regulation, were activated over twofold and less than 0.5% of genes were downregulated. For comparison, the cellular effects of coxsackievirus B4 and poliovirus 1 infections were studied in HeLa-Ohio cells by cDNA arrays. Gene activation patterns detected in the host cells during the infection by the three enteroviruses were only partially similar.

N-myc downstream-regulated gene 1 (NDRG1) mutations cause Charcot-Marie-Tooth disease type 4D (CMT4D). However, the cellular function of NDRG1 and how it causes CMT4D are poorly understood. We report that NDRG1 silencing in epithelial cells results in decreased uptake of low-density lipoprotein (LDL) due to reduced LDL receptor (LDLR) abundance at the plasma membrane. This is accompanied by the accumulation of LDLR in enlarged EEA1-positive endosomes that contain numerous intraluminal vesicles and sequester ceramide. Concomitantly, LDLR ubiquitylation is increased but its degradation is reduced and ESCRT (endosomal sorting complex required for transport) proteins are downregulated. Co-depletion of IDOL (inducible degrader of the LDLR), which ubiquitylates the LDLR and promotes its degradation, rescues plasma membrane LDLR levels and LDL uptake. In murine oligodendrocytes, Ndrg1 silencing not only results in reduced LDL uptake but also in downregulation of the oligodendrocyte differentiation factor Olig2. Both phenotypes are rescued by co-silencing of Idol, suggesting that ligand uptake through LDLR family members controls oligodendrocyte differentiation. These findings identify NDRG1 as a novel regulator of multivesicular body formation and endosomal LDLR trafficking. The deficiency of functional NDRG1 in CMT4D might impair lipid processing and differentiation of myelinating cells.

Tamoxifen, as a standard treatment of estrogen receptor (ER)-positive breast cancer, reduces breast cancer mortality by 31%. In 50% of advanced ER-positive cancers, however, de novo resistance exists and in 40% of patients with initial response acquired resistance frequently evolves. In order to explore mechanisms underlying endocrine therapy resistance in breast cancer and to identify new opportunities to treat patients, we created seven tamoxifen-resistant breast cancer cell line models that represent the luminal A subtype (MCF-7, T-47D, ZR-75-1), which expresses ER, and the luminal B subtype (BT-474), which additionally expresses the HER2 oncoprotein. We then performed drug sensitivity and resistance testing (DSRT), exome-sequencing and network analysis on all these cancer cell lines to determine the molecular and drug response profiles specific to tamoxifen resistance. As the cells became tamoxifen-resistant, we observed increasing sensitivity towards drugs like ERK1/2-, proteasome- and BCL-family inhibitors, but each of the isogenic cell line pairs had its distinct genomic and drug response profile. We then studied the molecular profiles of the 7 drug-resistant variants by RNA-sequencing in comparison to their 4 isogenic parental cells. We could not detect any common significantly differentially expressed genes (more than 2 fold change) across all the cell lines. However, the cell lines could be grouped into two different categories: The ones with low amount of differentially expressed genes, < 800 genes in the BT-474 and ZR-75-1 cell line pairs (with a 1,2% overlap), and the ones with high amount of differentially expressed genes, >1800 genes in T-47D and MCF-7 (with 7,3% overlap). Further analysis with the Ingenuity pathway analysis tool revealed an involvement of “Fatty Acid Activation” as well as “Stearate Biosynthesis I” in this group. Additionally, we discovered that genes involved in iron metabolism (TFRC, IREB2 and FTL) or iron-regulated genes like CP and NDRG1 are deregulated. These genes and pathways thereby provide avenues to identify new drug vulnerabilities for the tamoxifen resistant cancer cells, which we are now investigating at gene and protein level e.g. by image-based phenotyping. In summary, by combining drug testing data with the RNA-sequencing results, we hope to provide a number of potential drugs as well as matching biomarkers for planning clinical trials for patients with tamoxifen-resistant breast cancer. Citation Format: Susanne Hultsch, Sara Kangaspeska, Matti Kankainen, Vilja Pietiäinen, Olli Kallioniemi. Systematic drug testing and RNA-sequencing of tamoxifen resistant breast cancer cell lines. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2935.

Binding of echovirus 1 (EV1, a nonenveloped RNA virus) to the α2β1 integrin on the cell surface is followed by endocytic internalization of the virus together with the receptor. Here, video-enhanced live microscopy revealed the rapid uptake of fluorescently labeled EV1 into mobile, intracellular structures, positive for green fluorescent protein-tagged caveolin-1. Partial colocalization of EV1 with SV40 (SV40) and cholera toxin, known to traffic via caveosomes, demonstrated that the vesicles were caveosomes. The initiation of EV1 infection was dependent on dynamin II, cholesterol, and protein phosphorylation events. Brefeldin A, a drug that prevents SV40 transport, blocked the EV1 infection cycle, whereas drugs that disrupt the cellular cytoskeleton had no effect. In situ hybridization revealed the localization of viral RNA with endocytosed viral capsid proteins in caveosomes before initiation of viral replication. Thus, both the internalization of EV1 to caveosomes and subsequent events differ clearly from caveolar endocytosis of SV40 because EV1 uptake is fast and independent of actin and EV1 is not sorted further to sER from caveosomes. These results shed further light on the cell entry of nonenveloped viral pathogens and illustrate the use of viruses as probes to dissect caveolin-associated endocytic pathways.

Integrins are cell surface receptors for several microbial pathogens including echovirus 1 (EV1), a picornavirus. Cryo-electron microscopy revealed that the functional domain (alpha(2)I) of human alpha(2)beta(1) integrin binds to a surface depression on the EV1 capsid. This three-dimensional structure of EV1 bound to alpha(2)I domain provides the first structural details of an integrin interacting with a picornavirus. The model indicates that alpha(2)beta(1) integrin cannot simultaneously bind both EV1 and the physiological ligand collagen. Compared with collagen binding to the alpha(2)I domain, the virus binds with a 10-fold higher affinity but in vitro uncoating of EV1 was not observed as a result of attachment of alpha(2)I. A molecular model, constructed on the basis of the EV1-integrin complex, shows that multiple alpha(2)beta(1) heterodimers can bind at adjacent sites around the virus 5-fold symmetry axes without steric hindrance. In agreement with this, virus attachment to alpha(2)beta(1) integrin on the cell surface was found to result in integrin clustering, which can give rise to signaling and facilitate the initiation of the viral entry process that takes place via caveolae-mediated endocytosis.

Summary The High Throughput Biomedicine (HTB) unit at FIMM Technology Centre provides a wide range of biomedical high throughput assays. In collaboration with research groups and the Hospital District of Helsinki and Uusimaa, we have set up drug sensitivity and resistance testing (DSRT) platform with a set of 450 approved and investigational oncology drugs on patient samples. Ex vivo drug testing is currently performed at FIMM with primary cancer cells from patients with leukaemia and multiple myeloma as well as cancer cell lines and the drug sensitivity responses are integrated with molecular profiling such as exome sequencing, transcriptomics and phosphoproteomics. Currently, DSRT is run in 384-well plate format in 5 concentrations for each drug or in 1536-well plate format with 9 concentrations per drug and the assay plates are pre-drugged using acoustic droplet ejection with Labcyte Echo550. The viability and cell death of cells is measured after 72 h and results are analysed using Dotmatics Studies – software and an in-house developed interface, Breeze. Application of the platform to AML patient samples has uncovered taxonomic drug-response subtypes and individualised therapy based on DSRT has resulted in several clinical responses. The DSRT platform enables drug repositioning, provides new combinatorial possibilities and allows for linking drug sensitivities to predictive biomarkers. We are developing the platform with additional readouts and increasing the number of drugs. Our Labcyte Access Workstation, with Echo550 and Echo525 integration, allows development and set-up of miniaturised follow-up assays, such as reverse-phase protein array and qPCR, using non-contact acoustic dispensing.

The High Throughput Biomedicine (HTB) unit at the Institute for Molecular Medicine Finland FIMM was established in 2010 to serve as a national and international academic screening unit providing access to state of the art instrumentation for chemical and RNAi-based high throughput screening. The initial focus of the unit was multiwell plate based chemical screening and high content microarray-based siRNA screening. However, over the first four years of operation, the unit has moved to a more flexible service platform where both chemical and siRNA screening is performed at different scales primarily in multiwell plate-based assays with a wide range of readout possibilities with a focus on ultraminiaturization to allow for affordable screening for the academic users. In addition to high throughput screening, the equipment of the unit is also used to support miniaturized, multiplexed and high throughput applications for other types of research such as genomics, sequencing and biobanking operations. Importantly, with the translational research goals at FIMM, an increasing part of the operations at the HTB unit is being focused on high throughput systems biological platforms for functional profiling of patient cells in personalized and precision medicine projects.